{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scripts.forest import Forest\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "\n",
    "from sklearn import mixture\n",
    "from scipy.linalg import qr\n",
    "import scipy.stats as scp\n",
    "from math import cos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_dimension(arr, dim):\n",
    "    if dim == 0:\n",
    "        return arr\n",
    "    _, n_pts = np.shape(arr)\n",
    "    added = np.random.randint(low=0, high = 50, size = ( dim, n_pts))\n",
    "    return np.vstack((arr,added))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.          2.71828183  7.3890561  20.08553692]\n"
     ]
    }
   ],
   "source": [
    "A = [0,1,2,3]\n",
    "print(np.exp(A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_log_normal(n_Gaussians, n_pts):\n",
    "    means = []\n",
    "    covs = []\n",
    "    for i in range(n_Gaussians):\n",
    "        a = 4*i\n",
    "        b = np.random.randint(0, 3 * n_Gaussians)\n",
    "        c = np.random.randint(0, 1000)\n",
    "        means.append([a,b])\n",
    "        cov = [[1,cos(c)], [cos(c), 2]]\n",
    "        covs.append(cov)\n",
    "    G_x = np.zeros((n_Gaussians, n_pts))\n",
    "    G_y = np.zeros((n_Gaussians, n_pts))\n",
    "    pts_l = []\n",
    "    for i in range(n_Gaussians):\n",
    "        G_x[i], G_y[i] = np.random.multivariate_normal(means[i], covs[i], n_pts).T\n",
    "        pts_l.append(np.vstack((G_x[i], G_y[i])))    \n",
    "    pts = np.hstack(pts_l)\n",
    "    exp_pts = np.zeros(np.shape(pts))\n",
    "    for i in range(2):\n",
    "        exp_pts[i,:] = np.exp(pts[i,:])\n",
    "    return pts, exp_pts, means, covs\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#n_pts per Gaussian\n",
    "def sample_points(n_Gaussians, n_pts, noisy_dim):\n",
    "    means = []\n",
    "    covs = []\n",
    "    for i in range(n_Gaussians):\n",
    "        #a = np.random.randint(0, 3 * n_Gaussians)\n",
    "        a = 4*i\n",
    "        b = np.random.randint(0, 3 * n_Gaussians)\n",
    "        #b = 4*i\n",
    "        c = np.random.randint(0, 1000)\n",
    "        means.append([a,b])\n",
    "        cov = [[1,cos(c)], [cos(c), 2]]\n",
    "        #cov = [[0.3 + 3*i,0.1],[0.1,0.1 + 3*i]]\n",
    "        covs.append(cov)\n",
    "    G_x = np.zeros((n_Gaussians, n_pts))\n",
    "    G_y = np.zeros((n_Gaussians, n_pts))\n",
    "    pts_l = []\n",
    "    for i in range(n_Gaussians):\n",
    "        G_x[i], G_y[i] = np.random.multivariate_normal(means[i], covs[i], n_pts).T\n",
    "        pts_l.append(np.vstack((G_x[i], G_y[i])))    \n",
    "    pts = np.hstack(pts_l)\n",
    "    noisy_pts = add_dimension(pts, noisy_dim)\n",
    "    return pts, noisy_pts, means, covs\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_truth(pts, means, covs):    \n",
    "    dist = []\n",
    "    n_G = len(means)\n",
    "    print(\"num of gaussians is \", n_G)\n",
    "    for i in range(n_G):\n",
    "        dist.append(scp.multivariate_normal(mean = means[i], cov = covs[i]))\n",
    "    ptsT = np.transpose(pts)\n",
    "    t_scores = np.zeros(len(ptsT))\n",
    "    for i in range(len(t_scores)):\n",
    "        for j in range(n_G):\n",
    "            t_scores[i] += dist[j].pdf(ptsT[i])\n",
    "    t_indices = np.argsort(t_scores)[:100]\n",
    "    #plt.figure(figsize=(10,10))\n",
    "    #plt.title(\"ground truth\")\n",
    "    #plt.plot(pts[0,:], pts[1,:], 'ro')\n",
    "    #plt.plot(pts[0,t_indices], pts[1,t_indices], 'go')\n",
    "    #plt.savefig(\"mixture_ground_truth.pdf\")\n",
    "    #plt.show()\n",
    "    return t_indices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_iso(noisy_pts):\n",
    "    rng = np.random.RandomState(27)\n",
    "    _, n_p = np.shape(noisy_pts)\n",
    "    clf = IsolationForest(max_samples = 100, random_state = rng, contamination = 0.1, n_estimators= 30, behaviour = \"new\")\n",
    "    clf.fit(np.transpose(noisy_pts))\n",
    "    Y = clf.predict(np.transpose(noisy_pts))\n",
    "    iso_indices = []\n",
    "    for i in range(len(Y)):\n",
    "        if Y[i] == -1:\n",
    "            iso_indices.append(i)\n",
    "    return iso_indices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_density(noisy_pts):\n",
    "    _, n_p = np.shape(noisy_pts)\n",
    "    kwargs = {'max_depth': 8, 'n_trees':20,  'max_samples': 200, 'max_buckets': 3, 'epsilon': 0.1, 'sample_axis': 1, 'threshold': 0 }\n",
    "    forest = Forest(**kwargs)\n",
    "    forest.fit(noisy_pts)\n",
    "    gsw_indices, outliers, scores , pst = forest.predict(noisy_pts, 0.1)\n",
    "    print(forest.n_leaves)\n",
    "    return gsw_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lof(noisy_pts):\n",
    "    lof = LocalOutlierFactor(n_neighbors=10, contamination=0.1)\n",
    "    Z = lof.fit_predict(np.transpose(noisy_pts))\n",
    "    lof_indices = []\n",
    "    for i in range(len(Z)):\n",
    "        if Z[i] == -1:\n",
    "            lof_indices.append(i)\n",
    "    return lof_indices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1, -1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "       -1,  1,  1])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.transpose(noisy_pts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_em(noisy_pts, n_g):\n",
    "    mlf = mixture.GaussianMixture(n_components=n_g, covariance_type='full')\n",
    "    mlf.fit(np.transpose(noisy_pts))\n",
    "    Z = -mlf.score_samples(np.transpose(noisy_pts))\n",
    "    mix_indices = np.argsort(Z)[-100:]\n",
    "    return mix_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_figure(pts, indices, fig_title):\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.title(fig_title)\n",
    "    plt.plot(pts[0,:], pts[1,:], 'ro')\n",
    "    plt.plot(pts[0,indices], pts[1,indices], 'go')\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_Gaussians = 1\n",
    "noisy_dim = 1\n",
    "n_pts = 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num of gaussians is  1\n",
      "[128.  64. 101.  90. 113.  73. 115. 106.  92. 106.  83. 136. 102. 161.\n",
      " 109. 125. 107. 130. 115. 120.]\n",
      "num of gaussians is  1\n",
      "[ 92.  90. 136. 110. 104. 107. 146. 135. 102.  94. 115.  90. 112.  82.\n",
      " 108. 122. 110. 104.  90.  85.]\n",
      "num of gaussians is  1\n",
      "[ 98. 120. 112. 144. 115. 117. 132. 105.  80.  86.  84. 129. 150.  98.\n",
      "  91.  86. 126. 122.  97.  97.]\n",
      "num of gaussians is  1\n",
      "[ 97. 115. 133. 133. 118. 154. 122. 133. 166. 128. 119. 113. 108.  80.\n",
      " 161. 135. 100.  86. 112. 104.]\n",
      "num of gaussians is  1\n",
      "[111. 114. 130. 123. 130. 116.  99. 114. 100. 102. 152. 123. 122.  99.\n",
      " 100.  80.  90. 128. 113. 118.]\n",
      "num of gaussians is  1\n",
      "[138. 115.  88. 114. 112.  99. 120. 133.  93.  86.  65.  83.  90. 133.\n",
      "  70. 145. 114. 161.  80. 115.]\n",
      "num of gaussians is  1\n",
      "[ 99. 101.  98.  95. 137.  87. 136.  96. 103. 107. 128. 149. 101. 133.\n",
      " 133. 143. 145. 116. 119. 128.]\n",
      "num of gaussians is  1\n",
      "[111. 109. 131. 100. 128.  78.  87. 116. 145. 101. 129.  88. 110. 113.\n",
      "  54. 138. 115. 153. 102. 148.]\n",
      "num of gaussians is  1\n",
      "[ 98.  89. 102. 111. 129. 121.  95.  64. 130. 120. 143. 152.  88.  96.\n",
      " 121. 120. 133.  94. 108. 159.]\n",
      "num of gaussians is  1\n",
      "[110. 137. 151.  78. 119.  92.  95. 109. 109. 137. 103. 106. 116. 104.\n",
      " 105. 120. 119. 100. 118. 105.]\n",
      "GSW: 79.1\n",
      "ISO: 70.3\n",
      "LOF 48.4\n"
     ]
    }
   ],
   "source": [
    "av =0\n",
    "iv = 0\n",
    "lof=0\n",
    "ex = 10\n",
    "for _ in range(ex):\n",
    "    #pts, noisy_pts, means, covs = sample_points(n_Gaussians, n_pts, noisy_dim)\n",
    "    pts = np.random.randn(1000)\n",
    "    noisy_pts = np.zeros((2,1000))\n",
    "    noisy_pts[0,:] = pts\n",
    "    noisy_pts[1,:] = np.random.randint(low=0, high = 1000, size = 1000)\n",
    "    noisy_pts[1,:]= (noisy_pts[1,:] - 500)/300\n",
    "    #t_indices = get_truth(pts, means, covs)\n",
    "    t_indices = get_truth(pts, [0],[1])\n",
    "    iso_indices = get_iso(noisy_pts)\n",
    "    gsw_indices = get_density(noisy_pts)\n",
    "    lof_indices = get_lof(noisy_pts)\n",
    "    #em_indices = get_em(noisy_pts, n_Gaussians)\n",
    "    av += len(set(gsw_indices).intersection(set(t_indices)))\n",
    "    iv += len(set(iso_indices).intersection(set(t_indices)))\n",
    "    lof += len(set(lof_indices).intersection(set(t_indices)))\n",
    "print(\"GSW:\", av/ex)\n",
    "print(\"ISO:\", iv/ex)\n",
    "print(\"LOF\", lof/ex)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(noisy_pts[0,:], noisy_pts[1,:], 'ro')\n",
    "plt.plot(noisy_pts[0,t_indices], noisy_pts[1,t_indices], 'go')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(noisy_pts[0,:], noisy_pts[1,:], 'ro')\n",
    "plt.plot(noisy_pts[0,gsw_indices], noisy_pts[1,gsw_indices], 'go')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11bfbcd68>]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(noisy_pts[0,:], noisy_pts[1,:], 'ro')\n",
    "plt.plot(noisy_pts[0,lof_indices], noisy_pts[1,lof_indices], 'go')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11fd89e80>]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(noisy_pts[0,:], noisy_pts[1,:], 'ro')\n",
    "plt.plot(noisy_pts[0,iso_indices], noisy_pts[1,iso_indices], 'go')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num of gaussians is  1\n",
      "[112.  80.  83.  89. 105.  88. 101. 104. 110.  56.  81. 108. 102. 106.\n",
      " 116.  55.  42.  94.  97.  62.]\n",
      "num of gaussians is  1\n",
      "[113.  74. 103. 105. 104.  73. 115. 119.  63.  99.  92. 117.  58.  47.\n",
      "  96.  86.  63. 108.  77.  76.]\n",
      "num of gaussians is  1\n",
      "[101. 106. 101.  69. 112. 108.  75.  86.  76.  75. 106.  75. 106.  95.\n",
      " 100.  82.  93.  72.  58.  78.]\n",
      "num of gaussians is  1\n",
      "[ 73.  92.  91.  91.  72. 106.  82.  57.  62.  73.  63.  80.  88.  77.\n",
      "  90.  83.  95.  74.  97.  82.]\n",
      "num of gaussians is  1\n",
      "[ 63.  75. 116. 102. 101.  84.  83.  74.  95.  99. 106.  86.  82.  58.\n",
      "  85.  91. 121.  96.  79. 113.]\n",
      "num of gaussians is  1\n",
      "[ 53.  90.  86.  95. 111.  60.  98.  84. 101.  62.  94.  78.  68.  85.\n",
      "  61.  70.  87.  69.  69.  55.]\n",
      "num of gaussians is  1\n",
      "[128.  66. 100.  86.  62.  72.  94. 103. 111. 120.  82. 104.  67.  81.\n",
      "  77.  98.  92.  71.  81.  84.]\n",
      "num of gaussians is  1\n",
      "[ 98.  83.  48.  79.  60.  68.  81.  69.  50.  95.  79. 104.  91.  94.\n",
      "  70.  80.  82.  87.  67. 103.]\n",
      "num of gaussians is  1\n",
      "[112.  80.  83.  89. 105.  88. 101. 104. 110.  56.  81. 108. 102. 106.\n",
      " 116.  55.  42.  94.  97.  62.]\n",
      "num of gaussians is  1\n",
      "[113.  74. 103. 105. 104.  73. 115. 119.  63.  99.  92. 117.  58.  47.\n",
      "  96.  86.  63. 108.  77.  76.]\n",
      "num of gaussians is  1\n",
      "[101. 106. 101.  69. 112. 108.  75.  86.  76.  75. 106.  75. 106.  95.\n",
      " 100.  82.  93.  72.  58.  78.]\n",
      "num of gaussians is  1\n",
      "[ 73.  92.  91.  91.  72. 106.  82.  57.  62.  73.  63.  80.  88.  77.\n",
      "  90.  83.  95.  74.  97.  82.]\n",
      "num of gaussians is  1\n",
      "[ 63.  75. 116. 102. 101.  84.  83.  74.  95.  99. 106.  86.  82.  58.\n",
      "  85.  91. 121.  96.  79. 113.]\n",
      "num of gaussians is  1\n",
      "[ 53.  90.  86.  95. 111.  60.  98.  84. 101.  62.  94.  78.  68.  85.\n",
      "  61.  70.  87.  69.  69.  55.]\n",
      "num of gaussians is  1\n",
      "[128.  66. 100.  86.  62.  72.  94. 103. 111. 120.  82. 104.  67.  81.\n",
      "  77.  98.  92.  71.  81.  84.]\n",
      "num of gaussians is  1\n",
      "[ 98.  83.  48.  79.  60.  68.  81.  69.  50.  95.  79. 104.  91.  94.\n",
      "  70.  80.  82.  87.  67. 103.]\n",
      "num of gaussians is  1\n",
      "[112.  80.  83.  89. 105.  88. 101. 104. 110.  56.  81. 108. 102. 106.\n",
      " 116.  55.  42.  94.  97.  62.]\n",
      "num of gaussians is  1\n",
      "[113.  74. 103. 105. 104.  73. 115. 119.  63.  99.  92. 117.  58.  47.\n",
      "  96.  86.  63. 108.  77.  76.]\n",
      "num of gaussians is  1\n",
      "[101. 106. 101.  69. 112. 108.  75.  86.  76.  75. 106.  75. 106.  95.\n",
      " 100.  82.  93.  72.  58.  78.]\n",
      "num of gaussians is  1\n",
      "[ 73.  92.  91.  91.  72. 106.  82.  57.  62.  73.  63.  80.  88.  77.\n",
      "  90.  83.  95.  74.  97.  82.]\n",
      "GSW: 51.95\n",
      "ISO: 56.7\n",
      "LOF 45.5\n"
     ]
    }
   ],
   "source": [
    "n_Gaussians = 1\n",
    "n_pts = 1000\n",
    "noisy_dim = 0\n",
    "av =0\n",
    "iv = 0\n",
    "lof=0\n",
    "ex = 20\n",
    "for _ in range(ex):\n",
    "    pts, exp_pts, means, covs = sample_log_normal(n_Gaussians, n_pts)\n",
    "    noisy_pts = pts\n",
    "    t_indices = get_truth(pts, means,covs)\n",
    "    iso_indices = get_iso(exp_pts)\n",
    "    gsw_indices = get_density(exp_pts)\n",
    "    lof_indices = get_lof(exp_pts)\n",
    "    #em_indices = get_em(noisy_pts, n_Gaussians)\n",
    "    av += len(set(gsw_indices).intersection(set(t_indices)))\n",
    "    iv += len(set(iso_indices).intersection(set(t_indices)))\n",
    "    lof += len(set(lof_indices).intersection(set(t_indices)))\n",
    "print(\"GSW:\", av/ex)\n",
    "print(\"ISO:\", iv/ex)\n",
    "print(\"LOF\", lof/ex)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11fd90e48>]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = iso_indices\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(exp_pts[0,:], exp_pts[1,:], 'ro')\n",
    "plt.plot(exp_pts[0,ind], exp_pts[1,ind], 'go')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
